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The economics of coupled farm subsidies under costly and imperfect enforcement

2000· article· en· W2066621112 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueAgricultural Economics · 2000
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicEconomics of Agriculture and Food Markets
Canadian institutionsUniversity of Saskatchewan
Fundersnot available
KeywordsEnforcementSubsidyPublic economicsCheatingEconomicsMicroeconomicsDeterrence theoryEconomic interventionismIncentiveAgency (philosophy)ImperfectBusinessMarket economy

Abstract

fetched live from OpenAlex

Abstract This study relaxes the assumption of perfect and costless policy enforcement found in traditional agricultural policy analysis and introduces enforcement costs and cheating into the economic analysis of output subsidies. Policy design and implementation is modeled in this paper as a sequential game between the regulator who decides on the level of intervention, an enforcement agency that determines the level of policy enforcement, and the farmer who makes the production and cheating decisions. Analytical results show that farmer compliance is not the natural outcome of self‐interest and complete deterrence of cheating is not economically efficient. The analysis also shows that enforcement costs and cheating change the welfare effects of output subsidies, the efficiency of the policy instrument in redistributing income, the level of government intervention that transfers a given surplus to agricultural producers, the socially optimal income redistribution, and the social welfare from intervention.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.815
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.011
GPT teacher head0.173
Teacher spread0.162 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it